Estimating traveler volume by evaluating aerial images

ABSTRACT

Estimates may be compiled of a volume of travelers in an area, such as vehicles, pedestrians, and migratory wildlife. Such estimates are often compiled through human observation and/or the deployment of regional monitoring equipment, such as roadside cameras and road-embedded sensors; however, such techniques may entail significant costs in equipment purchase, deployment, monitoring, and maintenance, and may exhibit inadequate accuracy and/or rapidity of data collection. Presented herein are techniques for estimating a traveler volume in an area by using an aerial device, such as a drone, to capture an aerial image of the area from an aerial perspective, and applying object recognition machine vision techniques to recognize and count the travelers depicted in the aerial image. Such data may be used to estimate traveler volume; to evaluate transit patterns of the travelers throughout a region; and/or to control transit patterns of the travelers using transit control devices.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority under 35 U.S.C. §119(e) to U.S.Patent Application No. 61/946,962, filed on Mar. 3, 2014, the entiretyof which is incorporated by reference as if fully rewritten herein.

BACKGROUND

Within the field of computing, many scenarios involve an estimation of avolume of travelers in an area, such as a number of vehicles in a road;a transit pattern of visitors in a parking lot of a business; themovement of a population of pedestrians at an event; or a migratorypattern of a set of wildlife. In such scenarios, the traveler volume, aswell as other properties such as the direction, speed, and travelpatterns of the travelers, may be estimated through a variety oftechniques, such as human observation; tagging and tracking ofindividual travelers; and cameras or other detectors positionedthroughout the area. However, such techniques may involve significantcosts in terms of equipment purchase, deployment, monitoring, andmaintenance, and may also exhibit insufficient accuracy and/ortimeliness in the collected data about the traveler volume. For example,it may be desirable to adjust transit control devices in an area inorder to balance a flow of vehicular traffic. However, data about thevolume and fluctuation of vehicular traffic in various areas may not beattainable in a reliable and rapid manner using such techniques, whichmay limit the accuracy and responsiveness of transit control measures.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key factors oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

A volume of travelers in an area may be evaluated through the use ofaerial imaging. In particular, the recent development of dronetechnology has improved the affordability and sophistication of suchdevices that an aerial drone may capture aerial images of the area froman aerial perspective. Additionally, the development of objectrecognition machine vision techniques enables the recognition of objectsin an image in a comparatively reliable and computationally efficientmanner. The conjunction of these technological developments enables theprovision of new techniques for estimating a volume of travelers in anarea.

As a first example of the techniques presented herein, a device mayestimate traveler volume in area. The device may receive an aerial imageof the area captured from an aerial perspective; invoke an imageevaluator with the aerial image to recognize travelers in the aerialimage; count the travelers recognized in the aerial image; and estimatethe traveler volume of the area according to the count of the travelersand an area size of the area depicted in the aerial image, in accordancewith the techniques presented herein.

As a second example of the techniques presented herein, an aerialdevice, such as a drone, may evaluate and report to a transit service atraveler volume of an area. The aerial device may navigate to an aerialperspective of the area, and may then, using a camera, capture an aerialimage of the area from the aerial perspective. The aerial device mayalso invoke an image evaluator with the aerial image to recognizetravelers in the aerial image, and count the travelers recognized in theaerial image. The aerial device may then transmit the count of thetravelers to the transit service, in accordance with the techniquespresented herein.

As a third example of the techniques presented herein, a transit servermay be configured to estimate a traveler volume of an area. The transitserver may receive an aerial image of the area from an aerialperspective, and apply an image evaluator to the aerial image torecognize travelers in the image evaluator, and to count the travelersrecognized in the aerial image. The transit server may then estimate thetraveler volume of the area according to the count of the travelers andan area size of the area depicted in the aerial image, in accordancewith the techniques presented herein.

To the accomplishment of the foregoing and related ends, the followingdescription and annexed drawings set forth certain illustrative aspectsand implementations. These are indicative of but a few of the variousways in which one or more aspects may be employed. Other aspects,advantages, and novel features of the disclosure will become apparentfrom the following detailed description when considered in conjunctionwith the annexed drawings.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of an example scenario featuring an estimationof a traveler volume in an area.

FIG. 2 is an illustration of an example scenario featuring an estimationof a traveler volume in an area in accordance with the techniquespresented herein.

FIG. 3 is an illustration of an example method of estimating a travelervolume in an area, in accordance with the techniques presented herein.

FIG. 4 is an illustration of an example method of causing an aerialdevice, such as a drone, to estimate and report a traveler volume in anarea, in accordance with the techniques presented herein.

FIG. 5 is an illustration of an example system for estimating a travelervolume in an area, in accordance with the techniques presented herein.

FIG. 6 is an illustration of an example computer-readable mediumcomprising processor-executable instructions configured to embody one ormore of the provisions set forth herein.

FIG. 7 is an illustration of an example technique for classifyingrespective travelers in an area, in accordance with the techniquespresented herein.

FIG. 8 is an illustration of an example technique for tracking travelervolume and transit patterns in an area over time, in accordance with thetechniques presented herein.

FIG. 9 is an illustration of an example technique for tracking a transitpattern of an individual traveler while also anonymizing the individualtraveler, in accordance with the techniques presented herein.

FIG. 10 is an illustration of an example technique for controlling atransit pattern in a region using traveler volume estimated throughaerial images, in accordance with the techniques presented herein.

FIG. 11 is an illustration of an example computing environment whereinone or more of the provisions set forth herein may be implemented.

DETAILED DESCRIPTION

The claimed subject matter is now described with reference to thedrawings, wherein like reference numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the claimed subject matter. It may beevident, however, that the claimed subject matter may be practicedwithout these specific details. In other instances, structures anddevices are shown in block diagram form in order to facilitatedescribing the claimed subject matter.

A. Introduction

FIG. 1 is an illustration of an example scenario 100 featuringtechniques for estimating a volume of travelers 104 in an area 102, suchas vehicles traveling on a road during a heavy transit volume period. Inthis example scenario 100, it may be desirable to estimate the volume oftravelers 104 passing through the area 102 over time, e.g., in order toassess patterns in the utilization of the road; to assess a severity ofa traffic congestion problem, such as the effect of a vehicular accident106 on the flow of traffic on the road; to allocate developmentresources in an efficient manner; to allocate a toll over the volume oftravelers 104; and/or to enable transit control systems to promote ordiscourage travel through the area 102 in order to promote balance ofthe travel pattern of travelers 104 throughout a region.

To this end, various techniques may be utilized to estimate the travelervolume of travelers 104 in the area 102. As a first such example, atransit service 110 may deploy a set of roadside sensors 108, such astransit cameras or sensors that count the number of vehicles passing aparticular position in the area 102. A multitude of roadside sensors 108may be utilized to detect the transit flow of the travelers 104 overtime, e.g., deploying roadside sensors 108 at regular intervals in thearea 102, and having all such roadside sensors 108 report about thedetected transit patterns to the transit service 110. An alternative toroadside sensors 108 involves embedding pressure-sensitive equipment inthe surface of a road that detects the passage of travelers 104. As asecond such example, respective travelers 104 may be individually taggedwith devices that report their transit patterns to the transit service110. The reported data may be extrapolated from the small subset oftravelers 104 that report such data, to the full population of travelers104 in the area 102. In addition to a count of the travelers 104 in thearea 102, the roadside sensors 108 and/or traveler devices may reportother data to the transit service 110, such as the direction, location,speed, and/or acceleration of the respective travelers 104. As a thirdsuch example, an individual 112 may observe the area 102 and provide anapproximate estimate of the traveler volume in the area 102. These andother techniques may be utilized to estimate the traveler volume oftravelers 104 in the area 102.

Although such techniques may enable the estimation of traveler volume,several disadvantages may arrive therefrom. As a first such example,these methods may entail significant expense in terms of equipment (e g,implementing hundreds of fixed roadside and road-embedded sensors mayentail significant costs for equipment acquisition, deployment,operation, monitoring, and maintenance), and the use of individuals 112may entail a disproportionately large hourly cost. As a second suchexample, these methods may be prone to error; e.g., an individual 112may generate inaccurate and disproportionate estimates, and a firstestimate by a first individual 112 of an area 102 may conflict with asecond estimate by a second individual 112 for the same area 102.Roadside sensors 108 and/or pressure-sensitive equipment may exhibitinaccuracy (e.g., the roadside camera may be partially obstructed bydebris or weather elements, and pressure-sensitive equipment may countan 18-wheel vehicle several times while failing to count lightervehicles such as motorcycles), and a transit service 110 that utilizessuch equipment may produce incorrect traveler volume estimates.Estimates based on information transmitted by vehicles 104 may becontingent upon additional information about the proportion of suchreporting travelers 104 in the area 102, which may be difficult todetermine with certainty, and disparities in such information orassumptions thereof may lead to inaccurate estimates. As a third suchexample, these techniques may provide traveler volume data in a delayedmanner; e.g., a portion of a transit service 110 deployed in a remotearea may not have a direct connection with a transit monitoring station,and may only report data sporadically, only in lengthy intervals, oronly when visited by transit control personnel to retrieve the data.Therefore, the collection of such data may be suitable for surveying orhistorical study, but may be too slow for transit control management. Asa fourth such example, such equipment methods may be fixed at aparticular area 102, and traveler volume information about other areas102 within a region may involve additional equipment costs, costly andtime-consuming redeployment of existing equipment, and/or a significantdelay to implement. These and other disadvantages may arise from theestimation of traveler volume using the techniques depicted in theexample scenario 100 of FIG. 1.

B. Presented Techniques

FIG. 2 is an illustration of an example scenario 200 featuringtechniques for achieving an estimation 212 of traveler volume 216utilizing an aerial device 202, such as a drone, and a machine visiontechnique 208, such as object recognition applicable to an aerial image206 of the area 102, in accordance with the techniques presented herein.

In this example scenario 200, an aerial device 202, such as a drone, isnavigated to an aerial perspective over the area 102, and uses a camera204 to capture an aerial image 206 of the area 102. The aerial image 206of the area may be evaluated by an image evaluator 208, which usesmachine vision technique (e.g., an object recognition technique that iscapable of identifying the travelers 104 from an aerial perspective) toachieve an object recognition 210 of the travelers 104 presented in theaerial image 206. A device may then count the travelers 104 recognizedin the aerial image 206, which may be compared with an area size 216 ofthe area 102 depicted in the aerial image 206. Based on the objectrecognition 210 of the travelers 104 in the aerial image 206, anestimate 212 of traveler volume 216 in the area 102 may be achieved byidentifying a count 214 of the travelers 210 from the object recognition210 and the area size 216 of the area 102 to generate an estimate 212 ofthe traveler volume 218 of the travelers 104 in the area 102. Additionalinformation may also be generated from the capturing and comparison oneor more images 206, such as the determination of the direction, speed,acceleration, and transit patterns of the travelers 104 over time. Theestimates 212 of the traveler volume 218 may be utilized, e.g., toevaluate transit patterns of the travelers 104 through the area 102; toidentify problems arising in such transit patterns, such as causes oftraffic congestion and safety risks; to operate transit control devicesin the area 102 in order to adjust the transit of the travelers 104,such as transit signals and tolls; and/or to allocate the deployment ofdevelopment resources, such as adding and/or expanding roads, bridges,bypasses, and public transit systems to reduce patterns of trafficcongestion and to increase the traveler capacity of the region. Manysuch uses may be devised for the estimates 212 of traveler volume 218 inthe area 102 collected in accordance with the techniques presentedherein.

C. Technical Effects

The techniques presented herein may provide a variety of technicaleffects in the scenarios provided herein.

As a first such example, the techniques provided herein may enable thecollection of estimates 212 of traveler volume 218 in a comparativelycost-effective manner as compared with other techniques, such as thoseillustrated in the example scenario 100 of FIG. 1. For example, thecosts of acquiring and operating aerial devices, such as drones, tocapture and relay aerial images 206 of an area 102, and also thecomputational resources for applying a machine vision technique such asobject recognition to recognize and count the travelers 104 in an image210, are more affordable due to rapid development of these areas oftechnology and an expansion of commercial offerings. For example, suchequipment does not have to be developed for the specialized purpose ofestimating traveler volume 218 that raises the equipment costs, but maybe acquired as general-purpose equipment and reconfigured for the taskof estimating traveler volumes. Moreover, the deployment and return ofaerial devices 202 throughout the region 102 may reduce the exposure ofsuch equipment to adverse weather that damages and/or wears out suchequipment, as compared with fixed equipment such as roadside androad-embedded equipment. Moreover, the cost of deploying an aerialdevice 202 over an area 102 may be significantly less than for portableequipment that is transported to the area 102 by transit servicepersonnel.

As a second such example, the techniques provided herein may achieve amore estimate 212 of traveler volume 218 than may be achieved by othertechniques. For example, aerial images 206 captured from an aerialperspective may be less prone to visual obstruction than equipmentpositioned on the ground of the area 102, and less prone to errors ofsubjectivity as compared with estimates produced by individuals 112.

As a third such example, the techniques provided herein may enable amore rapid and flexible collection of estimates 212 of traveler volume218 than may be achieved by other techniques. For example, if anestimate 212 of traveler volume 218 is desired for a particular area102, an aerial device 202 may be navigated to the area 102 to collectand return the desired estimate 212, and the aerial devices 202 may bereadily redeployed to obtain an estimate 212 of traveler volume 218 fora second area 102. By contrast, portable equipment to the area 102 mayhave to be deployed by transit service personnel to the area 102, aswell as activated and configured, and then later retrieved by suchpersonnel, thereby incurring significant delays (particularly if suchtransit service personnel are also delayed in deploying the equipmentdue to heavy traveler volume 218 in the area 102). Additionally,equipment that is deployed to a remote area may not be continuouslyconnected to the transit service 110, such that data may be receivedfrom the equipment only sporadically, only over long intervals, and/oronly when transit service personnel may visit the equipment to retrievethe data. These and other technical advantages may arise from theestimation of traveler volume 218 in accordance with the techniquespresented herein.

D. Example Embodiments

FIG. 3 presents a first example embodiment of the techniques presentedherein, illustrated as an example method 300 of estimating a travelervolume 218 of travelers 104 in an area 102. The example method 300 maybe implemented on a device having a processor and an image evaluator208, such as a machine vision technique that uses object recognition toidentify travelers 104 in an aerial image 206. The example method 300may be implemented, e.g., as a set of instructions stored in a memorycomponent of the device (e.g., a memory circuit, a platter of a harddisk drive, a solid-state memory component, or a magnetic or opticaldisc) that, when executed by the processor of the device, cause thedevice to perform the techniques presented herein.

The example method 300 begins at 302 and involves executing 304 theinstructions on the processor. Specifically, the instructions cause thedevice to receive 306 an aerial image 206 of the area 102 captured froman aerial perspective. The instructions also cause the device to invoke308 the image evaluator 208 with the aerial image 206 to recognizetravelers 104 in the aerial image 206. The instructions also cause thedevice to count 310 the travelers 104 recognized in the aerial image206. The instructions also cause the device to estimate 312 the travelervolume 218 of the area 102 according to the count 214 of the travelers104 and an area size 216 of the area 102 depicted in the aerial image206. In this manner, the example method 300 causes the device togenerate an estimate 212 of traveler volume 218 in accordance with thetechniques presented herein, and so ends at 314.

FIG. 4 presents a second example embodiment of the techniques presentedherein, illustrated as an example method 400 of causing an aerial device202 to estimate a traveler volume 218 of travelers 104 in an area 102.The example method 400 may be implemented on an aerial device 202, suchas a drone, that features a processor, a camera 204, and an imageevaluator 208 that is capable of applying a machine vision techniqueinvolving object recognition to an aerial image 206 to identifytravelers 104 depicted in the aerial image 206. The example method 300may be implemented, e.g., as a set of instructions stored in a memorycomponent of the aerial device 202 (e.g., a memory circuit, a platter ofa hard disk drive, a solid-state memory component, or a magnetic oroptical disc) that, when executed by the processor of the aerial device202, cause the aerial device 202 to perform the techniques presentedherein.

The example method 400 begins at 402 and involves navigating 404 theaerial device 202 to an aerial perspective of the area 102. The examplemethod 400 also involves executing, on the processor of the aerialdevice 202, instructions that cause the aerial device 202 to perform avariety of tasks. As a first such task, the instructions cause theaerial device 202 to, using the camera 204, capture 408 an aerial image206 of the area 102 from the aerial perspective. As a second such task,the instructions cause the aerial device 202 to invoke 410 the imageevaluator 208 with the aerial image 206 to recognize travelers 104 inthe aerial image 206. As a third such task, the instructions cause theaerial device 202 to count 412 the travelers 104 recognized in theaerial image 206. As a fourth such task, the instructions cause theaerial device 202 to transmit 414 the count 214 of the travelers 104 tothe transit service 110. The example method 400 further involvesestimating 416 the traveler volume 218 (e.g., by the aerial device 202or a device of the transit service 110) according to the count 214 ofthe travelers 104 recognized in the aerial image 206 and an area size216 of the area 102 depicted in the aerial image 206. In this manner,the example method 400 of FIG. 4 achieves the estimation of the travelervolume 218 of travelers in the area 102 in accordance with thetechniques presented herein, and so ends at 418.

FIG. 5 presents an illustration of an example scenario 500 featuring athird example embodiment of the techniques presented herein, illustratedas an example server 502 comprising a system 510 that generates anestimate 212 of traveler volume 218 of travelers 104 in an area 102. Theexample system 510 may be implemented, e.g., on a server 502 having aprocessor 504, a communicator device that receives an aerial image 206of the area 102 captured by a camera 204 of an aerial device 202 from anaerial perspective. Respective components of the example system 510 maybe implemented, e.g., as a set of instructions stored in a memory 508 ofthe server 502 and executable on the processor 504 of the server 502,such that the interoperation of the components causes the server 502 tooperate according to the techniques presented herein.

The example system 510 comprises an image evaluator 512, whichrecognizes travelers 104 in the aerial image 206 (e.g., using a machinevision object recognition technique), and counts the travelers 104recognized in the aerial image 206. The example system 510 furthercomprises a traveler volume estimator 514, which estimates the travelervolume 218 of the area 102 according to the count 214 of the travelers104 and an area size 216 of the area 102 depicted in the aerial image206. In this manner, the interoperation of the components of the examplesystem 510 enables the server 502 to estimate the traveler volume inaccordance with the techniques presented herein.

Still another embodiment involves a computer-readable medium comprisingprocessor-executable instructions configured to apply the techniquespresented herein. Such computer-readable media may include, e.g.,computer-readable storage media involving a tangible device, such as amemory semiconductor (e.g., a semiconductor utilizing static randomaccess memory (SRAM), dynamic random access memory (DRAM), and/orsynchronous dynamic random access memory (SDRAM) technologies), aplatter of a hard disk drive, a flash memory device, or a magnetic oroptical disc (such as a CD-R, DVD-R, or floppy disc), encoding a set ofcomputer-readable instructions that, when executed by a processor of adevice, cause the device to implement the techniques presented herein.Such computer-readable media may also include (as a class oftechnologies that are distinct from computer-readable storage media)various types of communications media, such as a signal that may bepropagated through various physical phenomena (e.g., an electromagneticsignal, a sound wave signal, or an optical signal) and in various wiredscenarios (e.g., via an Ethernet or fiber optic cable) and/or wirelessscenarios (e.g., a wireless local area network (WLAN) such as WiFi, apersonal area network (PAN) such as Bluetooth, or a cellular or radionetwork), and which encodes a set of computer-readable instructionsthat, when executed by a processor of a device, cause the device toimplement the techniques presented herein.

An example computer-readable medium that may be devised in these ways isillustrated in FIG. 6, wherein the implementation 600 comprises acomputer-readable medium 602 (e.g., a CD-R, DVD-R, or a platter of ahard disk drive), on which is encoded computer-readable data 604. Thiscomputer-readable data 604 in turn comprises a set of computerinstructions 606 configured to operate according to the principles setforth herein. As a first such example, the computer instructions 606 maycause the device 610 to utilize a method of estimating a traveler volume218 of travelers 104 in an area 102, such as the example method 300 ofFIG. 3. As a second such example, the computer instructions 606 mayprovide a method of causing an aerial device 202 to estimate a travelervolume 218 of travelers 104 in an area 102, such as the example method400 of FIG. 4. As a third such example, the computer instructions 606may provide a system for estimating a traveler volume 218 of travelers104 in an area 102, such as the example system 510 in the examplescenario 500 of FIG. 5. Many such computer-readable media may be devisedby those of ordinary skill in the art that are configured to operate inaccordance with the techniques presented herein.

E. Variable Aspects

The techniques discussed herein may be devised with variations in manyaspects, and some variations may present additional advantages and/orreduce disadvantages with respect to other variations of these and othertechniques. Moreover, some variations may be implemented in combination,and some combinations may feature additional advantages and/or reduceddisadvantages through synergistic cooperation. The variations may beincorporated in various embodiments (e.g., the example method 300 ofFIG. 3; the example method 400 of FIG. 4; the example system 510 of FIG.5; and the example computer-readable storage device 602 of FIG. 6) toconfer individual and/or synergistic advantages upon such embodiments.

E1. Scenarios

A first aspect that may vary among embodiments of these techniquesrelates to the scenarios wherein such techniques may be utilized.

As a first variation of this first aspect, the techniques presentedherein may be used with many types of travelers 104, including vehiclessuch as automobiles, motorcycles, trucks, trains, buses, watercraft,aircraft, drones, and spacecraft; pedestrians, such as individuals in acrowd; and migratory wildlife. The techniques may also be utilized toestimate traveler volume 104 in many environments, such as a roadway,highway, parking lot, sidewalk, dirt or grass path, waterway, airspace,and an enclosed structure such as a shopping mall.

As a second variation of this first aspect, the volume of travelers inan area may be observed and calculated as many types of measurements,such as a count of travelers; a density of travelers in an area; a sizeor mass of the collection of travelers in an area; and/or a change ortrend in the number of travelers in an area.

As a third variation of this first aspect, the techniques providedherein may utilize a variety of aerial devices 202. Such aerial devices202 may be capable of remaining stationary while airborne, such as ahelicopter or balloon, or only of traveling to maintain lift, such as anairplane, and may travel at a variety of speeds, Such aerial devices 202may also be powered by various power sources, such as fuel, a chemicalor electric energy storage device, sunlight, or water or moisture, andmay either collect energy while remaining in the environment or mayreturn to the transit service for refueling.

As a fourth variation of this first aspect, many variations in thearchitecture of the provided techniques may be selected. As a first suchexample, the aerial device 202 may be operated autonomously (e.g., adrone that includes an autonomous navigation control system) and/or by ahuman operator, either on a continuous basis (e.g., a wirelesscommunication device may enable the human operator to interact with,control, and/or receive data from the aerial device 202 on a continuousbasis, either remotely or while positioned in the area 102) or aperiodic basis (e.g., the human operator may provide instructions to anotherwise autonomous aerial device 202, such as a selection of an area102 for which to evaluate traveler volume 26, and may later interactwith the aerial device 202 upon its return to receive the estimate 212of the traveler volume 218 and to reprogram the aerial device 202 forredeployment). As a second such example, a portion of the imageevaluation and/or traveler volume estimation may be distributed amongone or more aerial devices 202 and one or more ground-based devices,such as servers utilized by the transit service 110 during theestimation of traveler volume.

As a fifth variation of this first aspect, an aerial vehicle 202 maycapture an aerial image 206 of an area 102 using a variety of imagingtechniques, such as different portions of the electromagnetic spectrum(e.g., full-spectrum imaging; monochromatic imaging; thermal imaging inthe infrared range; and/or lidar detection), as well as other forms ofimaging, such as sonar or radar imaging. Such aerial images 206 may alsobe captured at a variety of zoom and/or focus levels, and may becaptured as a single aerial image 206 or a succession of aerial images206 in the same or different image modalities at the same or differenttimes, such as a monochromatic image and a thermal image, which may becompared to correlate different concurrent aerial images 206 for greateraccuracy or information, and/or to detect changes in the transitpatterns of the travelers 104 over time, Many such scenarios may bedevised to which the techniques presented herein may be advantageouslyutilized.

E2. Aerial Image Evaluation

A second aspect that may vary among embodiments of the techniquespresented herein involves the manner of evaluating the aerial image 206to perform the recognition and counting of the travelers 104 depictedtherein.

As a first variation of this second aspect, many image processingtechniques may be utilized to recognize and count the travelers 104 inthe aerial image 206. For example, the machine vision community hasdevised an extensive variety of object recognition techniques, basedupon spectral analysis, shape identification (e.g., identifying discretegeometric shapes in the image 104), comparison with prototypical imagesof recognizable options, and motion evaluation through a comparison ofimages captured over time. Such image processing may also utilize avariety of machine learning techniques, such as artificial neuralnetworks, genetic algorithms, and Bayesian classifiers, which may bedeveloped and trained to recognize a particular set of shapes and/orobjects in an image, such as aerial views of travelers 104, and theninvoked with the aerial image 206 to recognize such objects depicted inthe aerial image 206.

FIG. 7 presents an illustration of an example scenario 700 featuring asecond variation of this second aspect, wherein a machine visiontechnique, using an object classifier 704, may be used to identifytravelers 104 in a location 102 such as a parking lot of a business 702.In this example scenario 700, the object classifier 704 has beendeveloped to recognize the aerial appearance of travelers 104 appearingin an aerial image 206 captured from an aerial perspective by an aerialdevice 202, such as a drone. The object classifier 704 may comprise,e.g., an artificial neural network 710 that has been provided with atraining set that maps prototypical aerial depictions 708 of travelers104 in aerial images 704 to an identification thereof, and has beentrained to achieve such recognition for any aerial image 206. Moreover,in this example scenario 700, the artificial neural network 710 has beentrained to classify respective travelers 104 according to a travelertype 706; e.g., a first aerial depiction 708 of a traveler 104 may beprovided that maps to a first traveler type 704, such as a truck, and asecond aerial depiction 708 may be provided that maps to a secondtraveler type 704, such as an automobile. The object classifier 704 maybe applied to an aerial image 206 of the area 102 to detect thetravelers 104 within, thereby generating an estimate 212 of travelervolume 218 (e.g., the volume of vehicles in the parking lot of thebusiness 702) as compared with the capacity of the parking lot).Moreover, the object classifier 704 may classify the respectivetravelers 104 according to a traveler type 706, and may generate moredetailed estimates 212 of the numbers and/or proportion of therespective traveler types 706 among the population of recognizedtravelers 104. In this manner, an object classifier 704 may utilize anartificial neural network 710 to recognize, count, and/or classify thetravelers 104 depicted in an aerial image 206 of an area 102 inaccordance with the techniques presented herein.

As a third variation of this second aspect, in addition to generating anestimate 212 of the traveler volume 218 of an area 102, the techniquespresented herein may be utilized to generate additional informationabout the transit patterns of travelers 104 in the area 102. As a firstsuch example, in addition to estimating the traveler volume 218, animage evaluator 208 may also identify a transit direction and/or transitspeed of the respective travelers 104 recognized in the area 102 (e.g.,by identifying the same traveler 104 in a succession of aerial images206, and then comparing the positions and orientation of the traveler104 in the successive aerial images 206). Such identification may beperformed for individual travelers 104, and/or for a group or populationof travelers 104 en masse (e.g., determining that the traveler bodytogether is moving in a direction and/or at a particular transit speed).Further evaluation may be utilized to determine transit patterns amongthe travelers 104, e.g., sub-groups within a population of travelers 140that are moving and/or remaining stationary together through the area102. Estimates 212 of traveler volume 218 and transit patterns may alsobe aggregated in various ways; e.g., a period may be determined duringwhich the aerial image 206 of the area 102 was captured, and theestimate 212 of the traveler volume 218 for the period to a data setidentifying a typical traveler volume for the area 102 during respectiveperiods.

FIG. 8 presents an illustration of an example scenario involving arecognition of a transit pattern among travelers 104 within an area 102,such as vehicles and/or individuals depicted at different times inaerial images 206 of a parking lot of a business 702. In this examplescenario 104, the aerial device 202 captures a succession of aerialimages 206 at various times 802, and, in addition to estimating thetraveler volume 218 within the area 102, may compare the aerial images206 to detect transit patterns among such visitors, such as fluctuationsin the parking capacity of the area 102 during different periods (e.g.,on different days or at different times of day), and the direction oftravel of travelers 104 within the area 102. Additionally, a particulartraveler 804 may be identified as of interest, and the transit of thetraveler 804 in the area 102 may be evaluated by applying the imageevaluator 208 to recognize the traveler 804 in the succession of aerialimages 206. For example, the image evaluator 208 may determine thearrival of the traveler 804 at a second time 802 due to its firstappearance in a second aerial image 206 captured at the second time 802;a transit pattern 806 of the traveler 804 in the area 102, according toa comparison of the position of the traveler 804 in the succession ofaerial images 206; and/or a departure of the traveler 804 from the area102. Such information may inform a variety of evaluations, such as thesufficiency of the parking capacity of the area 102; a typical duration808 of a presence of a traveler 104 within the area 102; and/or safetyissues that may arise due to transit patterns of the travelers 104through the area 102, in accordance with the techniques presentedherein.

FIG. 9 presents an illustration of an example scenario 900 featuring athird variation of this second aspect, wherein a selected traveler 802may be tracked over time using an aerial device 202, in a manner that issensitive to the privacy of an individual. In this example scenario 900,a selected traveler 804 is identified for which a transit pattern of theselected traveler 804 through a particular area 102 is to be evaluatedover successive periods, such as correlating information aboutrespective visits of the selected traveler 804 to a business 702. Animage evaluator 208 may be able not only to recognize the traveler 804as a traveler 104 in successive aerial images 206 taken at differenttimes, but as the same selected traveler 804 during multiple instancesof transit through the area 102, e.g., by recognizing, in a particularportion 902 of an aerial image 206, a visual feature 904 of the selectedtraveler 804 that reveals the identity of the selected traveler 804(e.g., a facial feature of an individual, or a license plate of avehicle). The transit service 110 may therefore generate comparativeinformation about the successive instances of transit of the selectedtraveler 804 through the area 102. Because the privacy interests of theindividual may mitigate toward removal of the visual feature 904 fromstored aerial images 206, an anonymizer 906 may alter the portion 902 ofthe aerial image 206 comprising the visual feature 904 revealing theidentity of the selected traveler 804, and may generate an anonymizedaerial image 908 that obscures 910 the visual feature 904. Additionally,the identity of the individual 804 may be consistently tracked accordingto an anonymous identifier, such as a unique number that is arbitrarilyassigned to the selected traveler 804 to enable consistent trackingwithout revealing the identity of the selected traveler 804. Forexample, the transit service 110 may, upon detecting the selectedtraveler 804 in an aerial image 206, determine whether the identity ofthe selected traveler 804 is associated with an anonymous identifier912, and if not, may assign a new anonymous identifier 912 to theselected traveler 804. The transit service 110 may thereafter store anactivity record 914 of the selected traveler 804 in aerial images 206according to the anonymous identifier 912, thus generating an activityrecord 914 that documents the transit of the selected traveler 804 inthe area 102 while not revealing the identity of the selected traveler804.

As a fourth variation of this second aspect, the estimate 212 oftraveler volume 218 within an area 102 may provide further informationabout the nature of the traveler volume 218 and the area 102, such as anevent occurring within the area 102. For example, the traveler volume218 for a particular area 102 may be compared with a traveler volumethreshold (e.g., a maximum typical traveler volume 218 for the area102), such that an estimate 212 above the traveler volume threshold mayprompt further evaluation of the traveler volume 218 (e.g., upondetermining that the traveler volume exceeds the traveler volumethreshold, the transit service 110 may identify a transit eventoccurring in the area 102, such as the development of traffic congestionor an unexplained gathering of travelers 104 in a particular area 102).Additionally, respective transit events may be of a transit event typeselected from a transit event type set (e.g., traffic congestion arisingon a road due to a vehicular accident, construction, or an obstructionof the road by a weather event such as flooding). The transit service110 (e.g., a server and/or aerial vehicle 202) may, using the aerialimage 206 of the area 102, identify the transit event type of the event,and classify the transit event according to the transit event type. Manysuch types of information may be derived from the invocation of variousimage evaluators 208 with aerial images 206 of an area 102 in thecontext of estimating traveler volume 218 in accordance with thetechniques presented herein.

E3. Uses of Traveler Volume Estimates

A third aspect that may vary among embodiments of the techniquespresented herein involves uses of the estimates 212 of traveler volume218 generated in accordance with the techniques presented herein.

As a first variation of this third aspect, the area 102 may beassociated with an environment (e.g., a wildlife preserve, a residentialneighborhood, an industrial park, or an indoor environment such as amall), and estimates 212 of the traveler volume 218 may inform anenvironmental impact evaluator that evaluates an environmental impact ofthe traveler volume 218 on the environment. For example, estimates 212of traveler volume 218 may be correlated with wildlife stress and/orpopulation indicators, transit times, pollution levels, quality of lifein a residential neighborhood, and/or commercial business volume.

As a second variation of this third aspect, the area 102 may be a targetfor further development, e.g., expansion of a road network and/orpedestrian path area to increase capacity, improve traveler safety,and/or reduce volatility of traffic congestion. Estimates 212 oftraveler volume 218 in the area 102 may be utilized to allocateresources for such development, e.g., determine where and when transitpatterns create issues within the area 102, such that developmentresources may be allocated to expand capacity in areas 102 where suchexpansion is likely to ameliorate such problems.

FIG. 10 is an illustration of an example scenario 1000 featuring a thirdvariation of this third aspect, wherein a transit service 110 utilize atransit control adjuster to adjusts a transit restriction imposed by thetransit control, in proportion with the estimate 212 of the travelervolume 218 of the area 102. In this example scenario 1000, an area 102is associated with transit controls that impose transit restrictions onthe respective travelers 104 of the area 102. A transit service 110 maybe tasked with controlling such transit in order to reduce trafficcongestion in a region 1002, and may do so by utilizing transit controlsthat persuade travelers 104 to take a detour (e.g., detouring travelersfrom a primary route within the region 1002 to a secondary route thatmay be longer, but that may have less traffic congestion). For example,at a first time 1008, aerial devices 202 may be deployed above each area102 to generate estimates 212 of traveler volume 218 at the first time1008, while a transit toll 1004 is assessed to each traveler 104 in thearea 102. The estimates 212 of traveler volume 218 may indicate that thefirst area 102 exhibits significant traffic congestion, while the secondarea 102 exhibits comparatively light traveler volume 218. In order toreduce this disparity, at a second time, the transit tolls 1004 for therespective areas 102 may be adjusted (e.g., increasing the toll 1004 forthe first area 102 while reducing the toll 1004 for the second area 102)in order to persuade travelers 104 to choose a detour through the secondarea 102. The transit system 110 may transmit a signal to transitcontrol devices that collect the tolls 1004 from the travelers 104, andmay therefore instruct the transit control devices to adjust the tolls1004 in proportion with the estimate 212 of the traveler volume 218 ineach area 102. Additional estimates 212 of traveler volume 218 maycontinue to be collected, and the adjustment of the tolls 1004 mayreveal modest, but not adequate, redistribution of traveler volume 218.Accordingly, at a third time 1012, a second transit control device 1006may be adjusted, e.g., a stoplight that periodically restricts entry tothe first area 102, and thereby reduces traveler volume 218 therein. Forexample, where a detour area exist to the transit service 110 present totravelers an alternative to traveling through an area 102 having a hightraveler volume 218, and is associated with a transit control thatimposes a transit restriction on the respective travelers of the detourarea, the transit system 110 may utilize a detour transit controladjuster that adjusts the transit restriction of the transit control inproportion with the traveler volume 218 of the area 102.

As a fourth variation of this third aspect, a transit service 202 mayfurther comprise a transit event notifier, which, when the travelervolume exceeding a traveler volume threshold and indicating a transitevent, notifies a user of the transit event. The user may comprise,e.g., one or more travelers 104, transit control personnel for a transitservice 110, and/or first responders who may be tasked with attending tothe transit event. As a first such example, a device may present to theuser a region map indicating, for respective areas 102 of the region,the estimate 212 of the traveler volume 218 for the area 102; and thetransit event notifier may update the region map to indicate theestimate 212 of the traveler volume 218 of the area 102 on the map. As asecond such example, where a selected traveler 804 is associated with aroute through the area 102 to a destination, a route adjuster may adjustan estimated arrival time of the selected traveler 804 at thedestination according to the estimate 212 of the traveler volume 218 ofthe area 102. As a third such example, where a selected traveler 804 isassociated with a route through the area 102 to a destination, a detourpresenter may, responsive to the estimate 212 of the traveler volume 216exceeding a traveler volume threshold, present to the selected traveler804 an alternative route to the destination that does not pass throughthe area 102. These and other techniques may be utilized to notifyvarious users, such as transit system personnel and travelers 104through the area 102, of the estimates 212 of traveler volume 216 inaccordance with the techniques presented herein.

F. Computing Environment

FIG. 11 and the following discussion provide a brief, generaldescription of a suitable computing environment to implement embodimentsof one or more of the provisions set forth herein. The operatingenvironment of FIG. 11 is only one example of a suitable operatingenvironment and is not intended to suggest any limitation as to thescope of use or functionality of the operating environment. Examplecomputing devices include, but are not limited to, personal computers,server computers, hand-held or laptop devices, mobile devices (such asmobile phones, Personal Digital Assistants (PDAs), media players, andthe like), multiprocessor systems, consumer electronics, mini computers,mainframe computers, distributed computing environments that include anyof the above systems or devices, and the like.

Although not required, embodiments are described in the general contextof “computer readable instructions” being executed by one or morecomputing devices. Computer readable instructions may be distributed viacomputer readable media (discussed below). Computer readableinstructions may be implemented as program modules, such as functions,objects, Application Programming Interfaces (APIs), data structures, andthe like, that perform particular tasks or implement particular abstractdata types. Typically, the functionality of the computer readableinstructions may be combined or distributed as desired in variousenvironments.

FIG. 11 illustrates an example of a system 1100 comprising a computingdevice 1102 configured to implement one or more embodiments providedherein. In one configuration, computing device 1102 includes at leastone processing unit 1106 and memory 1108. Depending on the exactconfiguration and type of computing device, memory 1108 may be volatile(such as RAM, for example), non-volatile (such as ROM, flash memory,etc., for example) or some combination of the two. This configuration isillustrated in FIG. 11 by dashed line 1104.

In other embodiments, device 1102 may include additional features and/orfunctionality. For example, device 1102 may also include additionalstorage (e.g., removable and/or non-removable) including, but notlimited to, magnetic storage, optical storage, and the like. Suchadditional storage is illustrated in FIG. 11 by storage 1110. In oneembodiment, computer readable instructions to implement one or moreembodiments provided herein may be in storage 1110. Storage 1110 mayalso store other computer readable instructions to implement anoperating system, an application program, and the like. Computerreadable instructions may be loaded in memory 1108 for execution byprocessing unit 1106, for example.

The term “computer readable media” as used herein includes computerstorage media. Computer storage media includes volatile and nonvolatile,removable and non-removable media implemented in any method ortechnology for storage of information such as computer readableinstructions or other data. Memory 1108 and storage 1110 are examples ofcomputer storage media. Computer storage media includes, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, Digital Versatile Disks (DVDs) or other optical storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which can be used to storethe desired information and which can be accessed by device 1102. Anysuch computer storage media may be part of device 1102.

Device 1102 may also include communication connection(s) 1116 thatallows device 1102 to communicate with other devices. Communicationconnection(s) 1116 may include, but is not limited to, a modem, aNetwork Interface Card (NIC), an integrated network interface, a radiofrequency transmitter/receiver, an infrared port, a USB connection, orother interfaces for connecting computing device 1102 to other computingdevices. Communication connection(s) 1116 may include a wired connectionor a wireless connection. Communication connection(s) 1116 may transmitand/or receive communication media.

The term “computer readable media” may include communication media.Communication media typically embodies computer readable instructions orother data in a “modulated data signal” such as a carrier wave or othertransport mechanism and includes any information delivery media. Theterm “modulated data signal” may include a signal that has one or moreof its characteristics set or changed in such a manner as to encodeinformation in the signal.

Device 1102 may include input device(s) 1114 such as keyboard, mouse,pen, voice input device, touch input device, infrared cameras, videoinput devices, and/or any other input device. Output device(s) 1112 suchas one or more displays, speakers, printers, and/or any other outputdevice may also be included in device 1102. Input device(s) 1114 andoutput device(s) 1112 may be connected to device 1102 via a wiredconnection, wireless connection, or any combination thereof. In oneembodiment, an input device or an output device from another computingdevice may be used as input device(s) 1114 or output device(s) 1112 forcomputing device 1102.

Components of computing device 1102 may be connected by variousinterconnects, such as a bus. Such interconnects may include aPeripheral Component Interconnect (PCI), such as PCI Express, aUniversal Serial Bus (USB), firewire (IEEE 1394), an optical busstructure, and the like. In another embodiment, components of computingdevice 1102 may be interconnected by a network. For example, memory 1108may be comprised of multiple physical memory units located in differentphysical locations interconnected by a network.

Those skilled in the art will realize that storage devices utilized tostore computer readable instructions may be distributed across anetwork. For example, a computing device 1120 accessible via network1118 may store computer readable instructions to implement one or moreembodiments provided herein. Computing device 1102 may access computingdevice 1120 and download a part or all of the computer readableinstructions for execution. Alternatively, computing device 1102 maydownload pieces of the computer readable instructions, as needed, orsome instructions may be executed at computing device 1102 and some atcomputing device 1120.

G. Usage of Terms

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

As used in this application, the terms “component,” “module,” “system”,“interface”, and the like are generally intended to refer to acomputer-related entity, either hardware, a combination of hardware andsoftware, software, or software in execution. For example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration, both an application runningon a controller and the controller can be a component. One or morecomponents may reside within a process and/or thread of execution and acomponent may be localized on one computer and/or distributed betweentwo or more computers.

Furthermore, the claimed subject matter may be implemented as a method,apparatus, or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. Of course, those skilled inthe art will recognize many modifications may be made to thisconfiguration without departing from the scope or spirit of the claimedsubject matter.

Various operations of embodiments are provided herein. In oneembodiment, one or more of the operations described may constitutecomputer readable instructions stored on one or more computer readablemedia, which if executed by a computing device, will cause the computingdevice to perform the operations described. The order in which some orall of the operations are described should not be construed as to implythat these operations are necessarily order dependent. Alternativeordering will be appreciated by one skilled in the art having thebenefit of this description. Further, it will be understood that not alloperations are necessarily present in each embodiment provided herein.

Moreover, the word “example” is used herein to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as “example” is not necessarily to be construed as advantageousover other aspects or designs. Rather, use of the word example isintended to present concepts in a concrete fashion. As used in thisapplication, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or”. That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. In addition, the articles “a” and “an” as usedin this application and the appended claims may generally be construedto mean one or more unless specified otherwise or clear from context tobe directed to a singular form.

Also, although the disclosure has been shown and described with respectto one or more implementations, equivalent alterations and modificationswill occur to others skilled in the art based upon a reading andunderstanding of this specification and the annexed drawings. Thedisclosure includes all such modifications and alterations and islimited only by the scope of the following claims. In particular regardto the various functions performed by the above described components(e.g., elements, resources, etc.), the terms used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., that is functionally equivalent), even though notstructurally equivalent to the disclosed structure which performs thefunction in the herein illustrated example implementations of thedisclosure. In addition, while a particular feature of the disclosuremay have been disclosed with respect to only one of severalimplementations, such feature may be combined with one or more otherfeatures of the other implementations as may be desired and advantageousfor any given or particular application. Furthermore, to the extent thatthe terms “includes”, “having”, “has”, “with”, or variants thereof areused in either the detailed description or the claims, such terms areintended to be inclusive in a manner similar to the term “comprising.”

What is claimed is:
 1. A method of estimating a traveler volume of anarea using a device having a processor and an image evaluator, themethod comprising: executing, on the processor, instructions that causethe device to: receive an aerial image of the area captured from anaerial perspective; invoke the image evaluator with the aerial image torecognize travelers in the aerial image; count the travelers recognizedin the aerial image; and estimate the traveler volume of the areaaccording to the count of the travelers and an area size of the areadepicted in the aerial image.
 2. The method of claim 1, whereinexecuting the instructions further causes the device to, for therespective travelers recognized in the aerial image, identify a transitdirection for the traveler.
 3. The method of claim 1, wherein executingthe instructions further causes the device to, for the respectivetravelers recognized in the aerial image, identify a transit speed forthe traveler.
 4. The method of claim 1, wherein executing theinstructions further causes the device to, for the respective travelersrecognized in the aerial image, identify a duration of a presence of thetraveler within the area.
 5. The method of claim 1, wherein executingthe instructions further causes the device to: identify a period duringwhich the aerial image of the area was captured; and add the travelervolume for the period to a data set identifying a typical travelervolume for the area during respective periods.
 6. The method of claim 1,wherein: the respective travelers of the aerial image are of a travelertype selected from a traveler type set; and executing the instructionsfurther causes the device to, for the respective travelers recognized inthe aerial image: using the aerial image of the traveler, recognize thetraveler type of the traveler; and classify the traveler according tothe traveler type.
 7. The method of claim 1, wherein executing theinstructions further causes the device to: compare the traveler volumewith a traveler volume threshold; and upon determining that the travelervolume exceeds the traveler volume threshold, identify a transit eventfor the area.
 8. The method of claim 7, wherein: respective transitevents are of a transit event type selected from a transit event typeset; and executing the instructions further causes the device to: usingthe aerial image of the area, identify the transit event type of theevent; and classify the transit event according to the transit eventtype.
 9. The method of claim 1, wherein executing the instructionsfurther causes the device to: for a selected traveler depicted in theaerial image, identify a visual feature that reveals an identity of theselected traveler; and obscure the visual feature of the selectedtraveler in the aerial image.
 10. The method of claim 9, whereinexecuting the instructions further causes the device to: determinewhether the identity of the selected traveler is associated with ananonymous identifier; responsive to determining that the selectedtraveler is not associated with an anonymous identifier, assign a newanonymous identifier to the selected traveler; and store an activityrecord of the selected traveler in the aerial image according to theanonymous identifier.
 11. A method of causing an aerial device having aprocessor, a camera, and an image evaluator to report, to a transitservice, a traveler volume of an area, the method comprising: navigatingthe aerial device to an aerial perspective of the area; executing, onthe processor, instructions that cause the aerial device to: using thecamera, capture an aerial image of the area from the aerial perspective;invoke the image evaluator with the aerial image to recognize travelersin the aerial image; count the travelers recognized in the aerial image;and transmit the count of the travelers to the transit service; andestimating the traveler volume according to the count of the travelersand an area size of the area depicted in the aerial image.
 12. A transitserver that estimates a traveler volume of an area, the transit servercomprising: a processor; a communicator device that receives an aerialimage of the area from an aerial perspective; and a memory storinginstructions that, when executed by the processor, provides a systemcomprising: an image evaluator that: recognizes travelers in the aerialimage, and counts the travelers recognized in the aerial image; and atraveler volume estimator that estimates the traveler volume of the areaaccording to the count of the travelers and an area size of the areadepicted in the aerial image.
 13. The transit server of claim 12,wherein: the area is associated with an environment; and the systemfurther comprises: an environmental impact evaluator that evaluates anenvironmental impact of the traveler volume on the environment.
 14. Thetransit server of claim 12, wherein: the area is associated with atransit control that imposes a transit restriction on the respectivetravelers of the area; and the system further comprises: a transitcontrol adjuster that adjusts the transit restriction of the transitcontrol in proportion with the traveler volume of the area.
 15. Thetransit server of claim 14, wherein: the transit control furthercomprises a transit toll that is assessed to travelers in the area; andthe transit control adjuster adjusts the transit toll in proportion withthe traveler volume of the area.
 16. The transit server of claim 14,wherein: a detour area presents to travelers an alternative to travelingthrough the area, and is associated with a transit control that imposesa transit restriction on the respective travelers of the detour area;and the system further comprises: a detour transit control adjuster thatadjusts the transit restriction of the transit control in proportionwith the traveler volume of the area.
 17. The transit server of claim14, wherein the system further comprises: a transit event notifier that,responsive to the traveler volume exceeding a traveler volume thresholdand indicating a transit event, notifies a user of the transit event.18. The transit server of claim 17, wherein: the system furthercomprises a region map indicating, for respective areas of the region, atraveler volume for the area; and the transit event notifier furtherupdates the region map to indicate the traveler volume of the area onthe map.
 19. The transit server of claim 14, wherein: a selectedtraveler is associated with a route through the area to a destination;and the system further comprises: a route adjuster that adjusts anestimated arrival time of the user at the destination according to thetraveler volume of the area.
 20. The transit server of claim 14,wherein: a selected traveler is associated with a route through the areato a destination; and the system further comprises: a detour presenterthat, responsive to the traveler volume exceeding a traveler volumethreshold, presents to the user an alternative route to the destinationthat does not pass through the area.